WO2018088760A2

WO2018088760A2 - Method and device for adjusting quality determination conditions for test body

Info

Publication number: WO2018088760A2
Application number: PCT/KR2017/012408
Authority: WO
Inventors: 구대성; 김용; 박기원
Original assignee: 주식회사 고영테크놀러지
Priority date: 2016-11-14
Filing date: 2017-11-03
Publication date: 2018-05-17
Also published as: JP2021128174A; JP7302131B2; EP3540412A2; EP3540412A4; CN115112663A; EP3540412B1; US11199503B2; WO2018088760A3; EP3961332C0; EP3961332B1; EP3961332A1; US20190376906A1; JP2020500308A; CN109997028A; CN109997028B; KR20180054063A

Abstract

Disclosed are a method and a device for adjusting a quality determination conditions for a test body. The method for adjusting the quality determination conditions can include: a step for acquiring measurement values of the structures of a plurality of test bodies; a step for comparing error values of the measurement values with respect to design values of the structure with a prescribed reference value, and thereby determining for each of the plurality of test bodies, whether the test body is satisfactory or defective; a step for identifying one or more test bodies for which determination errors have occurred from among the plurality of test bodies; a step for generating and outputting a test result graph including the number of test bodies according to the error values, the reference value, and the number of the one or more test bodies for which the determination errors have occurred; a step for updating the reference value according to a graphical input on the test result graph so that the determination errors decrease; and a step for comparing the error values with the updated reference value and re-determining for each of the plurality of test bodies, whether the test body is satisfactory or defective.

Description

Method and apparatus for adjusting the conditions of acceptance judgment for the specimen

The present disclosure relates to a method and an apparatus for adjusting the acceptance judgment condition for a test object.

Manufacturers are striving to eliminate defective products during the production, assembly, intermediate and final assembly of products. In this process, manufacturers use a variety of inspection systems to determine whether a product is good (ie GOOD or NG).

According to one embodiment, the inspection system can determine the quality of the inspection object by measuring the structure of the inspection object and determining whether the measured value is within a preset range. For example, the inspection system irradiates light on the specimen and receives light reflected from the specimen to obtain image data of the specimen. In addition, the inspection system obtains a measurement value of the inspection object based on the acquired image data, and derives an inspection result for determining whether the inspection object is Good or NG based on the measured value and a preset reference value. do.

The inspection result derived by the inspection system may include a determination error that determines whether the inspection body that is actually good (False Call) or escapes the inspection object that is actually bad (Escape). In order to eliminate this determination error, the reference value used for the acceptance determination can be changed. However, in the related art, the measured value of the test object is simply displayed as a number on the display unit of the test system, and the reference value is often entered by the user directly. Thus, when the user wants to change the reference value, there is a problem in that the measured value displayed on the display unit must be input to the new value.

The present disclosure provides a method and apparatus for graphically displaying a test result for a test object and more conveniently adjusting a reference value used for the quality judgment.

In addition, the present disclosure provides a method and apparatus capable of visually displaying the inspection result of the inspection object, the acceptance determination result, and the examination result for the acceptance determination.

In addition, the present disclosure provides a method and apparatus that can visually indicate the change of the acceptance determination result and the examination result for the acceptance determination according to the adjustment of the reference value.

In addition, the present disclosure provides a method and apparatus for making the user input for reducing the quality of an inspection object's acceptance determination error more intuitively and conveniently.

One aspect of the present disclosure provides a method for adjusting a condition for determining an object for a test object in a condition determining device including a database, a processor, a user input unit, and an output unit. A method according to an exemplary embodiment includes the steps of obtaining, by a processing unit, measured values of structures of a plurality of test objects, and by the processing unit, comparing an error value of a measured value with respect to a design value of a structure and a predetermined reference value. Determining good or bad for each test object of the plurality of test objects, identifying, by the processing unit, one or more test objects in which a determination error has occurred among the plurality of test objects, and by the processing unit, a plurality according to the error value Generating a test result graph including the number of test subjects, a reference value, and the number of one or more test subjects in which a determination error occurred, and outputting the test result graph through an output unit, by the processing unit, one or more inspections in which a determination error occurred; Updating the reference value according to the graphical input through the user input on the test result graph, so as to reduce the number of sieves, and the processing unit By this, by comparing the error value and the updated reference value comprises the steps of a court good or bad for each test material of the plurality of inspection object. The determination error includes a first error in which the test object determined to be good is identified as bad and a second error in which the test object determined to be bad is identified as good.

According to at least one embodiment of the present disclosure, the test result and the reference value for the test object are graphically displayed, and the displayed reference value may be adjusted by the user's graphical input. In addition, the test result for the test object may be updated based on the reset reference value, and the updated test result may be displayed graphically. As a result, the user can adjust the reference value used for determining the quality of the test object more efficiently and simply.

According to at least one embodiment of the present disclosure, a graph showing the inspection result of the inspection object, the acceptance determination result, and the examination result for the acceptance determination is output, and an input for the user to reduce the acceptance determination error on the output graph. To do this. In addition, the change in the result of the review on the acceptance judgment according to the user's input is visually shown on the graph. As a result, it is possible for the user to more conveniently and intuitively check whether or not the acceptance judgment error is reduced.

According to at least one embodiment of the present disclosure, when the structure of the test object is measured and the determination is made according to the pre-update reference value, even if the reference value is updated according to a user input, the updated reference value is not required to be re-measured. It is possible to carry out the trial of the inspector. As a result, the transfer judgment according to the update of the reference value can be executed quickly.

1 is a view schematically showing an inspection system for determining an inspection object as good or defective according to an embodiment of the present disclosure.

2 is a view schematically showing the configuration of a measuring device for measuring the structure of the test body according to an embodiment of the present disclosure.

3 is a block diagram showing a detailed configuration of a good quality determination device for determining good quality of a test object according to an embodiment of the present disclosure.

4 is a diagram illustrating a test result list including a determination error according to an embodiment of the present disclosure.

FIG. 5 is a diagram illustrating a graph of test results showing a result of acceptance judgment and a result of a decision review according to an exemplary embodiment of the present disclosure.

6 is a diagram illustrating a reference value update on a test result graph according to an embodiment of the present disclosure.

7 is a diagram illustrating a graph of test results in which a partial region is enlarged according to an exemplary embodiment of the present disclosure.

FIG. 8 is a diagram illustrating a graph of test results showing a result of a judgment determination result and a judgment review result according to an exemplary embodiment of the present disclosure.

9 is a diagram illustrating a test result graph in which a reference value is updated according to an embodiment of the present disclosure.

10 is a flowchart illustrating a method of adjusting the acceptance judgment condition for a test object according to an embodiment of the present disclosure.

Embodiments of the present disclosure are illustrated for the purpose of describing the present disclosure. Embodiments of the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth below or the detailed description of these embodiments.

The term "part" as used herein refers to hardware components such as software, field-programmable gate arrays (FPGAs), and application specific integrated circuits (ASICs). However, "part" is not limited to hardware and software. The "unit" may be configured to be in an addressable storage medium, and may be configured to play one or more processors. Thus, as an example, "parts" means components such as software components, object-oriented software components, class components, and task components, and processors, functions, properties, procedures, subroutines, program code. Includes segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Functions provided within a component and "part" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts".

All technical and scientific terms used herein have the meanings that are commonly understood by one of ordinary skill in the art to which this disclosure belongs, unless otherwise defined. All terms used herein are selected for the purpose of more clearly describing the present disclosure and are not selected to limit the scope of the disclosure.

As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

Expressions such as “first” and “second” used in various embodiments of the present disclosure are used to distinguish a plurality of components from each other, but do not limit the order or importance of the components.

As used herein, expressions such as "comprising" and "having" are open-ended terms that include the possibility of including other embodiments unless specifically stated otherwise in the phrases or sentences in which the expressions are included. terms).

As used herein, the expression "based on" is used to describe one or more factors that affect the behavior or behavior of a decision or judgment described in the phrase in which the expression is included, which expression is used in the act of decision or judgment or It does not exclude additional factors that affect its behavior.

When a component is referred to herein as being "connected" or "connected" to another component, the component may be directly connected to or connected to the other component. It is to be understood that there may be new other components between the component and the other components.

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The same reference numerals are used for the same elements in the drawings, and duplicate descriptions of the same elements are omitted.

1 is a schematic diagram of an inspection system 10 for determining an inspection object as good or defective according to an embodiment of the present disclosure.

The inspection system 10 according to an exemplary embodiment of the present disclosure determines whether each of the plurality of inspection bodies 20 is a good product or a defective product, and classifies the product into the good storage device 30 or the defective product storage device 40 according to the determination result. Can be. Here, the test body 20 may be any manufactured article having a three-dimensional structure manufactured according to a predetermined design criterion. For example, the test body 20 may be a printed circuit board (PCB) on which electronic components are mounted.

The inspection system 10 may include the measurement apparatus 100, the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160. The inspection system 10 may also include a network 180 that communicates with the connection between the measurement device 100, the acceptance determination device 120, the determination review device 140, and the classification device 160. As shown in FIG. 1, the inspection body 20 passes through the measurement device 100, the determination review device 140, and the classification device 160 along the direction of the arrow, and the good storage device 30 or the defective product storage device ( 40).

According to one embodiment, the inspection system 10 may be installed at a rear end of a manufacturing stage for manufacturing the specimen 20 or a processing stage for processing the specimen 20. In this case, the inspection system 10 can determine whether the manufactured or processed inspection object 20 was manufactured according to a predetermined design criterion. In addition, the inspection system 10 can classify the good inspection body 20 into the goods storage device 30 and classify the defective inspection body 20 into the defective goods storage device 40 according to a determination result. have.

The measuring device 100 may generate a measured value measuring the structure (eg, the three-dimensional structure) of the test body 20. According to an embodiment, the measuring device 100 may measure the structure of the test object 20 using light. For example, the measuring device 100 irradiates structured light to the test body 20, receives light reflected from the test body 20, and measures the test body 20 based on the received light. Can generate image data. In addition, the measuring device 100 may generate a measured value measuring the structure of the test object 20 based on the image data. The measurement value generated by the measurement device 100 may be transmitted to the acceptance determination device 120 through the network 180. The configuration and operation of the measuring device 100 will be described in more detail with reference to FIG. 2.

The acceptance determination apparatus 120 may determine whether the test object 20 is good or bad. The quality determining device 120 may determine whether the measured value generated by the measuring device 100 is within a predetermined range, and thereby determine whether the test object 20 is good or bad. According to one embodiment, the acceptance determination device 120 may calculate an error value between the measured value and the design value of the structure of the test body 20. The adequacy determination device 120 determines that the test object 20 whose error value is equal to or less than a predetermined reference value is good, and determines that the test object 20 whose error value exceeds the predetermined reference value is defective (NG). Can be.

According to one embodiment, the acceptance determination apparatus 120 may determine that a part of the test object 20 of the test object 20 whose error value is equal to or less than a predetermined reference value (that is, good) is warned. . For example, when the error value of the test body 20 is in a predetermined range close to a predetermined reference value, the delivery judgment device 120 may determine the test object 20 as a warning.

The determination examination apparatus 140 can determine whether there is an error in the acceptance determination of the test object 20 by the acceptance determination apparatus 120. For example, there may be a case where the inspection object 20 determined as good by the delivery judgment device 120 is actually defective (Escape). In addition, the test body 20 judged to be defective by the acceptance determination apparatus 120 may actually be good (False Call). This determination error may occur when the reference value used for the acceptance determination in the acceptance determination apparatus 120 is not set appropriately. For example, when a predetermined reference value to be compared with an error value is set high, the affirmation determination device 120 can determine that the inspection object 20 that is actually defective is good, but the determination review device 140 determines such good judgment. This error can be determined. In addition, when the predetermined reference value compared with the error value is set low, the acceptance determination apparatus 120 can determine that the inspection body 20 which is actually good is defective, and the determination review apparatus 140 determines that such failure determination is an error. Can be judged.

The determination review device 140 may be implemented using a device capable of determining whether the test object 20 is actually good or bad. As an example, the determination review device 140 may include a device that can more accurately measure the structure of the test object 20. As another example, the determination review device 140 may include a device capable of confirming an electrical characteristic of the test object 20.

According to an exemplary embodiment, the determination reviewing apparatus 140 may determine whether a determination error occurs for a part of the inspection object 20 of the inspection object 20 in which the acceptance determination is made by the acceptance determination device 120. For example, the determination review apparatus 140 can determine whether there is a determination error with respect to the test object 20 that is determined to be warning or defective by the acceptance determination apparatus 120. In this case, the efficiency can be improved as compared with the case of determining whether a determination error for all the test object.

According to another embodiment, the determination review apparatus 140 may determine whether or not a determination error has been made for all the inspection objects 20 for which the acceptance determination has been made by the acceptance determination apparatus 120. In this case, the accuracy may be improved as compared with the case of determining whether a determination error for some of the inspection object. The examination result by the determination review apparatus 140 may be transmitted to the acceptance determination apparatus 120 through the network 180.

According to another embodiment, the determination review apparatus 140 estimates the good error value range of the good specimens 20 from the distribution of the measured error values, and additionally, the bad specimens 20. The range of defective error values can be estimated.

The error value of the physical property of the product produced according to a certain production process can be said to have a certain probability distribution. The error values of the good specimens 20 produced through a given process may have a good error value distribution expressed by, for example, a gamma distribution curve. The error values of the test specimens 20 that are defective due to problems outside the process may have a defective error value distribution expressed by, for example, a normal distribution curve.

Using this, the decision review apparatus 140 determines at least one probability distribution curve that fits most to the distribution of the measured error values, and selects a probability distribution curve closest to the origin among the determined probability distribution curves, with a good error. Value distributions can be considered and the remaining probability distribution curves (if any) can be regarded as bad error value distributions.

On the other hand, in the graphs of the good error distribution curve and the bad error distribution curve, the vertical axis is the number of specimens that must be natural numbers. Therefore, one or more specimens exist only within the horizontal axis range where the graph has a vertical axis value of 1 or more. If it is out of the horizontal axis range, it can be considered that the specimen exists in less than one sample, that is, it is absent. In some cases, the user may regard only a range of error values in which the number of specimen samples is a predetermined number or more as a significant error value range. In some cases, a user may consider only a range of test sample samples that contain a certain proportion of test sample samples (e.g., 99.5% test sample samples in order of decreasing error value) to be a significant error range. Can be.

According to this observation, the good error value range or the bad error value range which the user considers to be significant in the good error value distribution curve or the bad error value distribution curve can be estimated, respectively. In some cases, a significant distribution of defective error values may not be obtained, and thus, a range of meaningful defective error values may not be estimated.

Specifically, the decision review apparatus 140 may estimate the good error value distribution from a given distribution of sample error values using a predetermined probability distribution function and further estimate the bad error value distribution if necessary. At this time, the judgment review device 140 may estimate a good error value range in which the number of samples is 1 or more, for example, in the good error value distribution, and a bad error value range in which the number of samples is 1 or more, for example, in the bad error value distribution (if any). Can be estimated.

If the estimated good error value range and the bad error value range partially overlap each other, the determination review apparatus 140 considers a point where the good error value distribution and the bad error value distribution overlap each other, and thus the shortened good error value range. And error ranges can be estimated again.

If two or more defective error value distributions are estimated, the determination review apparatus 140 may re-establish the integrated defective error value ranges so as to encompass all estimated defective error value ranges from the defective error value distributions. .

The determination examination apparatus 140 makes an error in the acceptance determination of the test body 20 by the acceptance determination apparatus 120 based on the estimated good error value range, and further based on the defective error value range (if present). It can be determined whether there is.

For example, if the reference value of the acceptance judgment is within the good error value range, and if the error value of any test object 20 is larger than the reference value but is within the good error value range, the judgment review device 140 determines the test object. Although 20 is determined to be bad by the reference value, it can be determined that it is actually a false call of the second type which is good, and it can be determined that the current reference value is too strict.

For example, if the reference value of the acceptance judgment is within or exceeds the defective error value range, if the error value of a certain specimen 20 is smaller than the reference value but within the defective error value range, the determination review device 140 It can be determined that the test body 20 is an escape of the first type which is actually bad even though it is determined to be good by the reference value, and it can be determined that the current reference value is too relaxed.

Accordingly, the determination review apparatus 140 determines the number of the inspection objects 20 larger than the reference value while the error value is within the good error value range when the reference value of the good judgment is within the good error value range, When the reference value is within or exceeds the range of the defective error value, the number of the inspected objects that are the determination errors may be determined as the number of the inspected objects 20 that are smaller than the reference value while the error value is within the defective error value range.

The determination result by the determination review apparatus 140 may be transmitted to the acceptance determination apparatus 120 through the network 180. In addition, the good error value distribution and the good error value range estimated by the determination review device 140 may also be transmitted to the positive determination device 120 through the network 180. In addition, the defective error value distribution and the defective error value range estimated by the determination review apparatus 140 may also be transmitted to the contradictory determination apparatus 120 via the network 180.

In this way, even if the judgment review device 140 does not actually perform a rigorous retest on the samples judged to be defective, from the error value distribution detected in the samples at the beginning of production, the inspection objects (which are strongly estimated to have a determination error) ( 20) can be identified, and furthermore, the appropriateness of the current reference value can be determined.

The acceptance determination apparatus 120 according to an embodiment of the present disclosure adjusts the acceptance determination condition for the inspection object 20 so that the number of the inspection object 20 determined as the determination error by the determination review device 140 is reduced. Can be. The acceptance determination apparatus 120 is based on the acceptance determination result for the inspection object 20 generated by the acceptance determination device 120 and the determination review result for the inspection object 20 generated by the determination review device 140. The reference value compared with the error value can be updated. For example, when it is determined by the judgment review device 140 that a false call has occurred in at least a part of the test object 20, the fitness judgment device 120 may increase the reference value compared with the error value. have. In addition, when it is determined by the determination review device 140 that a determination error (Escape) has occurred in at least a part of the test object 20, the acceptance determination device 120 can lower the reference value compared with the error value.

The acceptance determination apparatus 120 may update the reference value compared with the error value according to the user input. The acceptance determination apparatus 120 can graphically display the acceptance determination result, the determination examination result, and the reference value for the inspection object 20. The user may provide the acceptance determination apparatus 120 with a graphic input for adjusting the reference value so that the number of the inspection objects in which the determination error has occurred is reduced, based on the acceptance determination result and the determination review result displayed graphically. The acceptance determination apparatus 120 may update the reference value in response to the graphical input of the user.

The acceptance determination apparatus 120 may judge the good or bad of the test object 20 by comparing the updated reference value with the error value. In addition, the pass / fail determination device 120 has an error in the judgment result of the inspection body 20 based on the determination review result generated by the determination review device 140 indicating whether the inspection body 20 is actually good or bad. It can be identified whether or not it has occurred. Accordingly, the acceptance determination apparatus 120 may graphically indicate the result of the acceptance judgment for the inspection object 20, the updated reference value and the number of inspection objects issued by the judgment error.

The acceptance determination apparatus 120 may be implemented using a computing device, for example, a server computer, a personal computer, a laptop computer, a smartphone, a tablet. The configuration and operation of the acceptance determination apparatus 120 will be described in more detail with reference to FIGS. 3 to 10.

The classification apparatus 160 may classify the test body 20 into the good storage device 30 or the defective product storage device 40. The classification apparatus 160 stores the good inspection body 20 and the bad inspection object 20 based on the judgment result in the acceptance determination device 120, respectively. Can be classified as

The network 180 enables the connection and communication between the measurement apparatus 100, the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160. The network 180 may be a wired network such as a local area network (LAN), a wide area network (WAN), or a value added network (VAN), a mobile radio communication network, a satellite, or the like. It can be implemented using any kind of wireless network such as a communication network, Bluetooth, Wireless Broadband Internet (Wibro), High Speed Downlink Packet Access (HSDPA), and the like.

Although each apparatus of the inspection system 10 is shown in FIG. 1 as a separate configuration, the present disclosure is not limited thereto, and any of the acceptance determination apparatus 120, the determination review apparatus 140, and the classification apparatus 160 may be used. At least some components of may be integrated into other devices. According to one embodiment, at least some components of the determination review apparatus 140 may be integrated into the acceptance determination apparatus 120. For example, the configuration of the determination review apparatus 140 that determines the acceptance determination error through estimation from the error value distribution may be implemented in the acceptance determination apparatus 120.

2 is a view schematically showing the configuration of a measuring device 200 for measuring the structure of a test body according to an embodiment of the present disclosure. According to the plurality of embodiments, the measuring device 200 of FIG. 2 may include all technical features of the measuring device 100 of FIG. 1. As shown in FIG. 2, the measuring device 200 includes an illumination unit 210, an imaging unit 220, and an image processing unit 230.

The illumination unit 210 irradiates the inspection object 22 with pattern light to measure the inspection object 22 that is a part of the inspection object 20. For example, the test body 20 is a printed circuit board, and the test object 22 is a solder formed on the printed circuit board or an electronic component mounted on the printed circuit board. However, the test body 20 and the test target 22 according to the present disclosure are not limited thereto, and may be any manufactured product having a three-dimensional structure.

In one embodiment, the lighting unit 210 includes a light source 211 for generating light, a grating element 212 for converting light from the light source 211 into pattern light, and a grating transfer for pitch conveying the grating element 212. And a projection lens 214 for projecting the patterned light converted by the instrument 213 and the grating element 212 onto the inspection object 22. For example, the grating element 212 is a predetermined distance (e.g., 2π / N; N is a natural number of two or more) by a grating transfer mechanism 213 such as a PZT actuator for phase shifting of patterned light. Can be transported by number.

As shown in FIG. 2, two lighting units 210 may be provided. However, the lighting unit 210 according to the present disclosure is not limited thereto and may be provided with one or three or more. When two or more lighting units 210 are provided, the plurality of lighting units 210 may be installed to be spaced at a predetermined angle along a circumferential direction or a virtual polygonal plane, or at regular intervals along a direction perpendicular to the test body 20. It may be installed to be spaced apart.

The imaging unit 220 may receive the light reflected by the inspection target 22 to obtain image data of the inspection target 22. The imaging unit 220 may be implemented using a charge coupled device (CCD) camera or a complementary metal oxide semiconductor (CMOS) camera, but is not limited thereto. The imaging unit 220 may be installed at an upper position perpendicular to the test body 20.

The image processor 230 processes the image data acquired by the imaging unit 220 to generate a measurement value of the structure of the inspection object 22. For example, the image processor 230 measures a horizontal length, a vertical length, a height length, an area, a volume, and the like of the inspection object 22 from the image data of the inspection object 22. The measurement value generated by the image processor 230 may be stored in the storage unit 232 of the image processor 230, or transmitted by the communicator 234 to the acceptance determination apparatus 120.

3 is a block diagram showing a detailed configuration of a good quality determination device 300 for determining the quality of a test object according to an embodiment of the present disclosure. According to the plurality of embodiments, the acceptance determination apparatus 300 of FIG. 3 may include all technical features of the acceptance determination apparatus 160 of FIG. 1. As shown in FIG. 3, the determination unit 300 according to an embodiment of the present disclosure includes a communication unit 310, an input / output unit 320, a processing unit 330, and a database 340.

The communication unit 310 may communicate with other devices, for example, the measurement device 100, the determination review device 140, and the classification device 160 of FIG. 1. In the communication unit 310, subcomponents for communicating with these devices may be integrated into one hardware device.

The input / output unit 320 is a configuration for interfacing with a user and includes a user input unit 322 and an output unit 324. The user input unit 322 may receive an input relating to the acceptance decision from the user. For example, the user input unit 322 may receive an input for adjusting the reference value used for the acceptance determination, an input for displaying the acceptance determination result, an input for selecting one of the acceptance determination result, and the like. The user input unit 322 may include a keyboard, a mouse, a touch pad, a touch screen, and the like.

The output unit 324 provides an output related to the acceptance decision to the user. For example, the output unit 324 may display the result of the acceptance decision of the test object 20, the reference value used for the acceptance determination, and the like. The output unit 324 may include a LCD (liduid crytal display), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, and the like.

The processor 330 may process data related to the acceptance decision. The processing unit 330 includes a quality determination unit 332, a determination result generation unit 334, and a determination reference adjustment unit 336. In addition, the database 340 is a configuration for storing data related to good or bad judgment, and includes a design value DB 342, a measured value DB 344, an error value DB 346, a reference value DB 348, and a determination result DB ( 350 and the decision review result DB 352.

The design value DB 342 stores the design values for all the inspection objects 22 of the test object 20. For example, when the test body 20 is a PCB, the design values include the width and length of the pad formed on the PCB, the volume and area of the solder placed on the pad, the height from the electronic component placed on the pad to the pad, and the like. The design value DB 342 may be stored.

In the measured value DB 344, the measured value about all the test | inspection objects 22 of the test body 20 is stored. The measurement value stored in the measurement value DB 344 may correspond to the design value stored in the design value DB 342. According to an embodiment, the measured value for the test body 20 may be generated by the measuring device 200 of FIG. 2. The measurement value generated in the measurement device 200 may be stored in the measurement value DB 344 through the communication unit 234 of the measurement device 200 and the communication unit 310 of the acceptance determination device 300.

The acceptance determination unit 332 may calculate an error value of the measured value with respect to the design value of the inspection target 22 of the inspection object 20. The adequacy determination unit 332 determines the design value of the inspection target 22 of the inspected object 20 stored in the design value DB 342 and the inspection object 22 of the inspected object 20 stored in the measured value DB 344. The difference between the measured values is calculated as an error value. The calculated error value may be stored in the error value DB 346.

The acceptance determination unit 332 may determine whether the structure of the test object 20 satisfies a predetermined criterion. The acceptance determination unit 332 may compare the error value stored in the error value DB 346 with the reference value stored in the reference value DB 348 to determine whether the specimen 20 is good or bad. According to one embodiment, the acceptance determination unit 332 determines that the test object 20 is good if the error value of the test object 20 of the test object 20 is less than or equal to the reference value of the test object 20. When the error value exceeds the reference value, the test body 20 may be determined to be defective. For example, when the reference value for the vertical length of the pad of the test object 20 is set to 0.5 mm, the positive determination unit 332 rejects the test object 20 having an error value of 0.6 mm with respect to the vertical length of the pad. It is determined that the test body 20 having an error value of 0.4 mm is good. The acceptance determination result generated by the acceptance determination unit 332 may be stored in the determination result DB 350.

In the decision examination result DB 352, a decision examination result indicating whether or not there is an error in the acceptance determination of the acceptance determination unit 332 with respect to the inspection target 22 of the inspection object 20 is stored. When an error occurs in the determination of the acceptance of the inspection object 22, a 'decision error' may be displayed as a result of the determination review of the inspection object 22. The determination error is a first type (Escape) in which the inspection target 22 determined as good by the acceptance determination unit 332 is actually defective, and the inspection target 22 determined as defective by the acceptance determination unit 332. ) Contains the second type (False Call) which is actually good.

According to one embodiment, the decision review result stored in the decision review result DB 352 may be generated by the decision review device 140 of FIG. 1. The communication unit 310 may receive the determination review result generated by the determination review device 140 and store the result in the determination review result DB 352. In addition, the communication unit 310 of the acceptance judgment device 300 also includes the good error value distribution, the good error value range, the bad error value distribution (if any), and the bad error value range (if any) estimated by the determination review device 140. ) Can be stored in the decision review result DB 352.

The determination result generator 334 may generate an inspection result graph indicating the number of inspection objects according to the error value. According to an embodiment, the test result graph may be a two-dimensional graph, in which the horizontal axis may represent an error value, and the vertical axis may represent the number of test bodies 20 having the corresponding error value. In addition, the determination result generation unit 334 may display, as a reference value, a graphical user interface (GUI) object that is movable by a user operation on the inspection result graph. For example, the GUI object representing the reference value may have a shape of a bar, an arrow, a line, a dot, a rectangle, or the like.

The determination result generation unit 334 can display the result of the determination of the quality of the inspection body 20 in the inspection result graph. According to an exemplary embodiment, the determination result generator 334 may indicate good, warning, and bad as a result of the positive determination on the test result graph. For example, the determination result generation unit 334 displays a region where the error value is equal to or less than the reference value on the inspection result graph, and displays a region where the error value exceeds the reference value as defective. In addition, the determination result generation unit 334 displays a predetermined area on the inspection result graph close to the reference value as a warning. In this case, the boundary of the area corresponding to the warning may be displayed on the test result graph. In addition, according to an exemplary embodiment, the determination result generator 334 may indicate the number of the test bodies 20 corresponding to good, warning, and bad on the test result graph.

The determination result generation unit 334 may display the result of the examination on the result of the acceptance determination on the inspection result graph. According to an exemplary embodiment, the determination result generator 334 may indicate an error of the first type (Escape) and an error of the second type (False Call) as the determination examination result on the inspection result graph. For example, the determination result generation unit 334 displays, as an error of the first type (Escape), an area to which the test object 20 determined as actually good among the areas where the error value exceeds the reference value on the inspection result graph. In addition, the determination result generation unit 334 displays an area of the test object 20 to which the test object 20, which is determined to be actually defective, among the areas where the error value is equal to or less than the reference value, as an error of the second type.

According to an embodiment, determination errors such as an error of the first type and an error of the second type may be determined through detailed inspection by the decision review apparatus 140. The determination examining device 140 may include a device capable of measuring the structure of the test body 20 more precisely, or a device capable of measuring the electrical characteristics of the test body 20. The determination examination device 140 may determine whether the test body 20 is actually good or bad by measuring the structural electrical characteristics of the test body 20 more precisely. As a result, the determination examination apparatus 140 discriminates | determines the test body 20 which is a real defect among the test body 20 determined to be good, and the test body 20 which is actually good among the test body 20 judged to be bad. can do.

According to another embodiment, determination errors may be determined through estimation from an error value distribution by the determination review apparatus 140. The determination review apparatus 140 determines at least one probability distribution curve fitted to the error value distribution of the test object 20 measured by the measuring apparatus 100, and the probability distribution closest to the origin among the determined probability distribution curves. The curve can be considered a good error distribution and the remaining probability distribution curves (if any) can be considered a bad error distribution. The determination review apparatus 140 may estimate the good error value range from the good error value distribution, and estimate the bad error value range from the bad error value distribution (if any). In addition, the determination examination apparatus 140 can discriminate | determine the test | inspection body 20 in which the determination error among the test body 20 based on the good error value range and the bad error value range (if present).

According to the above-described embodiment, the determination review apparatus 140 judges the acceptance determination error through estimation from the error value distribution, and the determination result generation unit 334 receives the acceptance determination error determined by the determination review apparatus 140. Although the test | inspection body 20 which the determination error generate | occur | produced is identified on the basis, this indication is not limited to this. For example, the determination result generation unit 334 may be implemented to determine whether the determination of the positive and negative determination by the estimation directly from the distribution of the error value, to identify the test object 20 in which the determination error.

According to an exemplary embodiment, the determination result generator 334 may determine the candidate reference value so that the reference value can be updated. The determination result generator 334 may determine at least one candidate reference value for reducing or minimizing the number of the inspected objects 20 in which the determination error has occurred. For example, the determination result generation unit 334 is configured such that the area corresponding to the error of the first type (Escape) or the error of the second type (False Call) is reduced or eliminated by updating the reference value with the candidate reference value. The candidate reference value can be determined. The determination result generator 334 may display the determined at least one candidate reference value on the test result graph. For example, the candidate reference value may be represented by a dot, a line, a rectangle, an arrow, or the like.

According to one embodiment, the determination result generation unit 334 determines the candidate reference value based on the good error value range and the bad error value range (if any) of the test object 20 estimated by the determination review device 140. Can be. If there is a bad error value range, the determination result generator 334 may determine a candidate reference value from among values greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range. If there is no defective error value range, the determination result generator 334 may determine a candidate reference value among certain values greater than or equal to the maximum value of the good error value range.

According to an embodiment, the user may select a predetermined area on the test result graph through the user input unit 322. In response to the user input received through the user input unit 322, the determination result generator 334 may enlarge the selected predetermined area and output the enlarged predetermined area through the output unit 324. For example, the enlarged predetermined area may be output to overlap the graph of the test result.

According to an exemplary embodiment, the determination result generator 334 may generate a test result list including at least one of a measured value, an error value, a positive judgment result, and a determination error review result for the test object 20. The determination result generator 334 may output the inspection result graph and the inspection result list through the output unit 324. The user may check the test result graph and the test result list through the output unit 324.

According to an embodiment, the user may select one test object 20 from the test result list through the user input unit 322. The determination result generator 334 may display an error value of the selected test object 20 in the test result graph in response to a user input received through the user input unit 322. For example, the error value of the selected specimen 20 may be represented by a dot, a line, a rectangle, an arrow, or the like. According to another embodiment, when there is no input regarding the selection of the test object 20 from the user, the test object 20 which has been most recently determined to be acceptable may be automatically selected. In this case, an error value of the test body 20 which has been judged most recently may appear on the test result graph.

The determination criterion adjusting unit 336 may update the reference value according to the input received from the user through the user input unit 322. According to an embodiment of the present disclosure, the determination criterion adjusting unit 336 may receive a graphic input from the user to move the position of the GUI object representing the reference value on the test result graph. For example, a user may use a mouse as the user input unit 322 to click and drag a movable bar-shaped GUI object indicating a reference value on a test result graph to a predetermined position. In this case, the decision criterion adjusting unit 336 may update the reference value to a value corresponding to the dragged predetermined position in response to the graphic input. According to another exemplary embodiment, the determination criterion adjusting unit 336 may receive a graphic input for designating a predetermined position on the test result graph from the user. For example, the user may click a predetermined position on the test result graph by using a mouse as the user input unit 322. In this case, the decision criterion adjusting unit 336 may update the reference value to a value corresponding to the clicked predetermined position in response to the graphic input. The updated reference value may be stored in the reference value DB 348 by the determination reference adjustment unit 336.

The acceptance determination unit 332 may re-determine good or bad for the test object 20 based on the reference value updated by the determination reference adjustment unit 336. According to one embodiment, the acceptance determination unit 332 judges the inspection body 20 as good when the error value of the inspection object 20 with respect to the inspection object 22 is less than or equal to the updated reference value, and the error value is updated. In the case where the reference value is exceeded, the test body 20 may be determined to be defective.

In addition, the acceptor determination unit 332 may identify the inspector 20 in which the judgment error has occurred among the inspector 20. According to one embodiment, the acceptance determination unit 332 may identify the inspection body 20 in which the judgment error has occurred, based on the judgment result of the inspection body 20 and the determination review result stored in the determination review result DB 352. Can be. For example, the adequacy determination unit 332 judges that the inspection object 20 is actually bad but is judged to be a good first type (Escape) error, and judges that the inspection object 20 is actually good but is determined to be bad. It can be determined that the error is of the second type (False Call).

The determination result generation unit 334 may display the reference value updated by the determination criterion adjustment unit 336, the result of the parliamentary judgment using the updated reference value, and the result of the examination review on the result of the parliamentary judgment in the test result graph. By checking the graph of the test result output through the output unit 324, the user may confirm that an error occurred in the pausing judgment using the updated reference value is reduced compared to an error occurred in the acceptance determination using the pre-update reference value.

As described above, the acceptance determination apparatus 300 according to the plurality of embodiments of the present disclosure graphically displays the acceptance determination result and the reference value for the test object, and may adjust the reference value by the graphical input of the user. In addition, the acceptance judgment apparatus 300 may execute the acceptance judgment on the inspected object based on the reset reference value, examine whether the acceptance judgment is free of errors, and graphically display the results of the acceptance judgment and the determination review thereof. . As a result, the user can adjust the reference value used for determining the quality of the test object more efficiently and simply.

4 is a diagram illustrating a test result list 400 according to an exemplary embodiment of the present disclosure. According to some embodiments, the inspection result list 400 of FIG. 4 may be generated by the determination result generation unit 334 of FIG. 3 and output through the output unit 324.

As shown in FIG. 4, the test result list 400 includes

test result data

410, 420, 430, 440, 450, and 460 for each of the plurality of test objects. Each test result

data

410, 420, 430, 440, 450, and 460 shows an object ID, an object ID, an object to be tested, a measurement object, a measurement value, an error value, a good judgment result, and a judgment review for the test object. Include the result.

The

test result data

410, 430, and 450 includes a measured value of a horizontal length of the 'pad 1' formed on the test object, and an error value of calculating a difference between the measured value and the design value 10.0 mm. It is assumed that the reference value used for determining whether the pad 1 has a horizontal length is set to 0.5 mm. Referring to the test result data 410, it is determined that the test object having the test piece ID '1' is good because the error value is 0.5 mm or less. On the other hand, referring to the

inspection result data

430 and 450, the specimens having the specimen IDs '2' and '459' are determined to be defective because their error values exceed 0.5 mm. Among them, it is determined that the inspector having the inspector ID '459' has an error of the second type (False Call) as a result of the acceptance decision examination. For example, in a test piece whose test piece ID is "459", the horizontal length of the "pad 1" is poor according to a predetermined determination criterion, but actually has good characteristics.

The

test result data

420, 440, and 460 include a measured value of measuring the longitudinal length of the 'pad 1' formed on the test object, and an error value of calculating a difference between the measured value and the design value of 10.0 mm. It is assumed that the reference value used for determining whether the pad 1 has a vertical length is set to 0.5 mm. Referring to the test result data 410, it is determined that the test piece having the test piece IDs '1', '2', and '459' is good because the error value is 0.5 mm or less. Among them, the inspector having the inspector ID '459' is judged to have an error of the first type (Escape) as a result of the acceptance judgment examination. For example, in a test piece whose test piece ID is "459", the vertical length of "Pad 1" is good according to a predetermined determination criterion, but actually has a bad characteristic.

FIG. 5 is a diagram illustrating an inspection result graph 500 showing a result of a dissatisfaction determination and a determination review, according to an exemplary embodiment. According to the plurality of embodiments, the test result graph 500 of FIG. 5 may be generated by the determination result generator 334 of FIG. 3 and output through the output unit 324. According to an exemplary embodiment, the test result graph 500 of FIG. 5 may input an input for selecting any test result data (for example, test result data 450) from the test result list 400 of FIG. 4. It may be generated in response to receiving through the user input unit 322.

As shown in FIG. 5, the horizontal axis of the test result graph 500 represents an error value, and the vertical axis represents the number of test objects. The test result graph 500 includes a curve 510 indicating the number of test objects having a corresponding error value. In addition, the test result graph 500 includes a first reference value GUI 520 indicating the first reference value used for the determination of the quality of the test object, and a test object used to determine a test object corresponding to a warning among the test objects determined to be good. A second reference value GUI 530 may be included that indicates the second reference value. For example, the second reference value may be set to 90% of the first reference value.

In addition, the test result graph 500 may include a sample error value indicator 540 indicating an error value p of a test sample of a particular test sample, for example, a user of particular interest. For example, the sample error value indicator 540 may indicate an error value of the test object selected from the test result list 400 of FIG. 4. As another example, the sample error value indicator 540 may indicate an error value of the most recently inspected specimen.

In the inspection result graph 500, the results of the acceptance determination and the determination review may be displayed. The inspection result graph 500 may display an area corresponding to good, warning, and error as a result of the acceptance decision, and an area corresponding to defective as a result of the decision examination. In addition, the test result graph 500 may also display the number of test objects corresponding to good, warning, error, and bad. As shown in FIG. 5, 352 specimens having an error value equal to or less than the first reference value b were judged as good, and 107 specimens having an error value exceeding the first reference value b were once determined to be defective. In particular, among the 352 specimens that were good, 57 specimens having an error value between the second reference value a and the first reference value b were classified as warning because they were determined to be good, but close to the first reference value. On the other hand, since the sixty test specimens having an error value between d and e deviate from the natural error distribution pattern in a given process, it may be determined that a defect has occurred due to a problem not belonging to a given process. However, since the error distribution of the 47 specimens whose error value is between the first reference value b and c is consistent with the natural error distribution of the 352 specimens that are good, the 47 specimens between b and c that were determined to be bad These may actually be good as a normal result of a given process. Accordingly, 47 test specimens between the first reference values b and c may be determined to be false calls of the second type. Indeed, to verify that such a determination is a second type of error, the user can select and examine a particular test object whose error value is p between the first reference value b and c.

If the first reference value is maintained at b, even if the process problem that causes the abnormal error value between d and e is solved, the error value will naturally be distributed between 0 and c in the normal process. If product production continues, a significant amount of subsequent products will be judged defective, with an error value constantly between the first reference values b and c. That is, in the example of Figure 5, if the product production process itself is not wrong, the first reference value for determining good and bad may not be excessively set without reflecting the natural error distribution characteristics of the product production process.

The test result graph 500 may include a candidate reference value indicator 550 indicating a candidate reference value for minimizing the number of test objects in which a determination error has occurred. The candidate reference value is a candidate of a reference value such that the number of specimens determined to be an error is minimum (eg, 0), and may be selected within a range in which an error value is between c and d. Although the candidate reference value indicator 550 is indicated by a dot in FIG. 5, the present invention is not limited thereto and may be displayed in various forms such as an arrow, a line, and a rectangle. In addition, although the candidate reference value indicator 550 is shown in the singular in FIG. 5, the present invention is not limited thereto, and a plurality of candidate reference value indicators 550 may be displayed.

6 is a diagram illustrating a test result graph 600 in which a reference value is updated according to an embodiment of the present disclosure. According to an embodiment, the test result graph 600 of FIG. 6 may be a reference value updated from the test result graph 500 of FIG. 5.

According to an embodiment, the user may update the reference value through the user input unit 322 on the test result graph 600. As an example, the user may drag the first reference value GUI 520 to the position of the candidate reference value indicator 550 on the test result graph 600 using the mouse as the user input unit 322. As another example, the user may touch the position of the candidate reference value indicator 550 on the test result graph 600 using the touch pad. As such, the position of the first reference value GUI 520 is moved in the test result graph 600 by the graphic input through the user input unit 322. In addition, as the position of the first reference value GUI 520 is moved, the first reference value may also be updated. For example, as shown in FIG. 6, the first reference value is updated from b to b '.

According to an embodiment, as the position of the first reference value GUI 520 is moved, the position of the second reference value GUI 530 may also be moved without a separate user input. For example, when the ratio of the second reference value to the first reference value is set to 90%, the second reference value GUI 530 may be moved to the right so that the second reference value is 90% of the updated first reference value. As shown in FIG. 6, by moving the first reference value GUI 520, the second reference value GUI 530 may be moved such that the second reference value is updated from a to 90% of b ′. According to another exemplary embodiment, the second reference value 530 may be moved by the graphic input through the user input unit 322 on the test result graph 600 to move the position of the second reference value GUI 530.

According to one embodiment, in response to the movement of the first and second

reference values GUI

520, 530, the acceptance determination unit 332 determines the acceptance judgment for each of the specimens based on the updated first and second reference values. You can run The acceptance determination unit 332 may determine that the error value of each of the test objects is less than or equal to b ′, which is the first reference value, and determine that the error value is defective if the error value of each of the test objects is more than b ′. In addition, the acceptance determination unit 332 may determine as a warning when an error value of each of the test objects is greater than a 'and less than b' which is the second reference value. In addition, the transfer decision unit 332 may identify a test object in which a trial error occurs in the test object.

The examination result graph 600 may show the results of the paternity trial and the results of the trial review. As shown in FIG. 6, 399 specimens having an error value of b 'or less are' good ', 12 specimens having an error value of greater than a' and 'b' or less are 'warned', and 60 specimens having an error value of d or more and e or less. Each is judged as 'bad'. Compared with the inspection result graph 500 of FIG. 5, the number of the test object which the error occurred with respect to the acceptance judgment changes from 47 in FIG. 5 to 0 in FIG. In other words, the first reference value used as the reference for the acceptance judgment of the test object is updated, thereby minimizing the error of the acceptance judgment.

In this way, the first reference value GUI 520 is moved by the graphical input of the user, so that the good determination device 300 can newly determine whether the inspection object according to the updated reference value. That is, unlike the conventional process in which the user has to visually check the measured value to determine a new reference value and input it as a numerical value, according to the present disclosure, the user can determine the new reference value while viewing the graph of the test result, and the graphic The reference value can be updated by positive input. As a result, since it is possible to change the first reference value by moving the first reference value GUI 520 in a state where the determination error is visually displayed, it is possible to correct the determination error quickly and conveniently. In addition, since the quality of the test object can be newly determined based on the updated reference value, user convenience can be attained.

7 is a diagram illustrating a graph of test results in which a partial region is enlarged according to an exemplary embodiment of the present disclosure. The inspection result graph 700 of FIG. 7 is a graph showing the result of the inspection and the result of the examination of the inspection object, and may be the same as the inspection result graph 500 of FIG. 5.

According to an embodiment, the user may enlarge at least a portion of the test result graph 700 output through the output unit 324 using the user input unit 322. As an example, the user may select a predetermined area 710 on the test result graph 700 by using a mouse as the user input unit 322. In response to selecting the predetermined region 710, the determination result generator 334 may generate an enlarged graph 720 in which the predetermined region 710 is enlarged. The generated enlarged graph 720 may be output through the output unit 324. The enlarged graph 720 may be output separately from the test result graph 700 or may be output to overlap on the test result graph 700.

8 is a diagram illustrating a test result graph 800 showing a result of a judgment decision and a decision review result according to an exemplary embodiment of the present disclosure. According to the plurality of embodiments, the test result graph 800 of FIG. 8 may be generated by the determination result generator 334 of FIG. 3 and output through the output unit 324. According to an embodiment, the test result graph 800 of FIG. 8 may input an input for selecting any one test result data (eg, test result data 460) from the test result list 400 of FIG. 4. It may be generated in response to receiving through the user input unit 322.

The test result graph 800 includes a curve 810 indicating the number of test objects having a corresponding error value, and a reference value GUI 820 indicating a reference value used for determining whether the test object is successful. As shown in FIG. 8, 330 specimens having an error value equal to or less than the first reference value d are determined to be 'good', and 129 specimens having an error value between e and f greater than the first reference value d are determined to be 'bad'. It became. At this time, among the 330 specimens that were good, 25 specimens with an error value between b and c were found to be good, but are outside the natural error distribution pattern (from 0 to a) in a given process. Some problems that do not belong to the process may be defective. Accordingly, twenty five specimens between b and c may be determined to be the first type of escape.

If the first reference value d is maintained, even if an abnormal problem in the process causing an abnormal error value between e and f is solved, the error value will naturally be distributed only between 0 and a. If production continues, a significant amount of subsequent products will be judged to be good, while being actually defective and consistently showing an error value lower than the first reference value d. That is, in the example of Figure 8, if the product production process itself is not wrong, the first reference value for determining good and bad may not be excessively set without reflecting the natural error distribution characteristics of the product production process.

The test result graph 800 may include a candidate reference value indicator 830 indicating a candidate reference value for minimizing the number of test objects in which a determination error has occurred. The candidate reference value is a candidate of the reference value such that the number of specimens determined to be an error is minimum (eg, 0), and may be selected within a range in which the error value is between a and b. In FIG. 8, the candidate reference value indicator 830 is indicated by a dot, but is not limited thereto, and may be displayed in various forms such as an arrow, a line, and a rectangle. In addition, although the candidate reference value indicator 830 is singularly displayed in FIG. 8, the present invention is not limited thereto, and a plurality of candidate reference value indicators 830 may be displayed. In addition, although not shown in FIG. 8, the inspection result graph 800 according to an embodiment includes a GUI indicating a reference value used to determine an inspection object corresponding to a warning among inspection objects determined to be good, and a plurality of inspections. It may include an indicator indicating the error value of any one of the sieve.

9 is a diagram illustrating a test result graph 900 in which a reference value is updated according to an embodiment of the present disclosure. According to an embodiment, the test result graph 900 of FIG. 9 may be a reference value updated from the test result graph 800 of FIG. 8.

According to an embodiment, the user may update the reference value through the user input unit 322 on the test result graph 900. As an example, the user may drag the reference value GUI 820 to the position of the candidate reference value indicator 830 on the test result graph 900 by using a mouse as the user input unit 322. As another example, the user may touch the position of the candidate reference value indicator 830 on the test result graph 900 using the touch pad. As such, the position of the reference value GUI 820 is moved in the test result graph 900 by the graphic input through the user input unit 322. In addition, the reference value may be updated as the position of the reference value GUI 820 is moved. For example, as shown in Fig. 9, the reference value is updated from d to d '.

According to one embodiment, in response to the reference value GUI 820 being moved, the acceptance determination unit 332 may execute the acceptance judgment for each of the specimens based on the updated reference value. The acceptance determination unit 332 may determine that the error value of each of the test objects is equal to or less than d ', which is a reference value, and determine that the error value is defective if the error value of each of the test objects is more than d'. In addition, the transfer decision unit 332 may identify a test object in which a trial error occurs in the test object.

In the test result graph 900, the results of the paternity trial and the results of the trial review may be displayed. As shown in FIG. 9, 305 test objects having an error value of d 'or less are determined to be' good ', and 154 test objects having an error value of more than d' are determined as 'bad'. Compared with the inspection result graph 800 of FIG. 8, the number of the test object which the error occurred with respect to the acceptance judgment changes from 25 in FIG. 8 to 0 in FIG. In other words, the reference value used as the reference for the acceptance decision of the test object is updated, thereby minimizing the error of the acceptance decision.

10 is a flowchart illustrating a method of adjusting the acceptance judgment condition for a test object according to an embodiment of the present disclosure. At least some of the steps shown in FIG. 10 may be performed by the configurations shown in FIGS.

First, in step S1000, the acceptance determination apparatus 300 obtains the measured values of the structures of the plurality of test objects. For example, the measuring apparatus 100 may irradiate light onto the test object, receive light reflected from the test object, and generate image data of the test object based on the received light. In addition, the measuring apparatus 100 may generate a measured value measuring the structure of the test object based on the image data. The acceptance determination apparatus 300 may obtain the measurement value generated by the measurement apparatus 100 through the communication unit 310.

Next, in step S1010, the acceptance determination unit 332 executes acceptance determination for each of the plurality of inspection objects. For example, the quality determining unit 332 may determine whether the measured value obtained in step S1000 is within a predetermined range, to determine whether the test object is good or bad. The acceptance determination unit 332 calculates an error value of the measured value with respect to the design value of the structure of the test object, and compares the calculated error value with a predetermined reference value. The acceptance determination unit 332 may determine that the test object whose error value is less than or equal to the predetermined reference value is good, and determine that the test object whose error value exceeds the predetermined reference value is defective (NG).

Next, in step S1020, the determination result generation unit 334 identifies the inspection object in which a determination error has occurred among the plurality of inspection objects. For example, the determination result generation part 334 is a test | inspection which the determination error of the some test body generate | occur | produced based on the quality determination result of the test object obtained in step S1010, and the determination examination result by the determination review apparatus 140. Identifies the sieve. Here, the determination error includes a first error in which the test object determined to be good is identified as actually defective, and a second error in which the test object determined to be bad is identified as actually good.

According to one embodiment, the determination review apparatus 140 determines the good error value distribution and the bad error value distribution (if any) based on the error value distribution of the test object 20 measured by the measuring device 100 and From the good error value distribution and the bad error value distribution (if any), the good error value range and the bad error value range (if any) can be estimated, respectively. In addition, the determination examination apparatus 140 can discriminate | determine the test | inspection body 20 in which the determination error among the test body 20 based on the good error value range and the bad error value range (if present). In addition, the determination result generation unit 334 can receive the determination examination result from the determination review apparatus 140 to identify the inspection object 20 in which the determination error has occurred.

Next, in step S1030, the determination result generation unit 334 outputs the inspection result graph. For example, the determination result generation unit 334 generates a inspection result graph indicating the number of inspection objects according to the error value. The test result graph is a two-dimensional graph, in which the horizontal axis represents an error value, and the vertical axis represents the number of test objects having the corresponding error value among the plurality of test objects. The determination result generation unit 334 may display, as a reference value, a bar-shaped GUI object that is movable by a user operation on the inspection result graph. In addition, the determination result generating unit 334 may indicate good, warning, and bad as a result of the positive determination on the inspection result graph. In addition, the determination result generation unit 334 may display the result of the examination on the result of the acceptance determination on the inspection result graph. In addition, the determination result generation unit 334 may determine and display at least one candidate reference value for minimizing the number of one or more specimens having a determination error on the examination result graph.

According to an embodiment, the candidate reference value may be determined based on the good error value range and the bad error value range (if present) estimated in step S1020. If there is a range of bad error values, the candidate reference value may be selected from any values that are greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range. If there is no bad error value range, the candidate reference value may be selected from any values greater than or equal to the maximum value of the good error value range.

Next, in step S1040, the determination criterion adjusting unit 336 updates the reference value according to the graphical input on the test result graph. The user may provide a graphical input on the test result graph to reduce the number of one or more test objects for which a decision error occurred. For example, the user may drag a GUI representing a reference value on the test result graph 600 to a predetermined position by using a mouse as the user input unit 322. The decision criterion adjusting unit 336 updates the reference value to the predetermined value in response to the graphical input of the user (that is, the movement of the GUI representing the reference value).

Next, in step S1050, the payment judgment unit 332 executes the transfer judgment for each of the plurality of test objects based on the updated reference value. For example, the acceptance determination unit 332 compares the reference value updated in step S1040 with the error value to judge good or bad for each test object of the plurality of test objects. In addition, the pass / fail determination unit 332 identifies a test object in which a trial error has occurred among the plurality of test objects. In addition, the determination result generation unit 336 shows the updated reference value and the number of the inspection bodies in which the judgment error occurred on the inspection result graph.

In the steps illustrated in FIG. 10, some steps may be omitted, two or more steps may be executed simultaneously, or an order of execution between the steps may be changed. In addition, although the method of adjusting the acceptance determination condition for the test object has been described through specific embodiments, the method may also be embodied as computer readable code on a computer readable recording medium. Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system. The computer-readable recording medium may include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. In addition, functional programs, codes, and code segments for implementing the above embodiments can be easily inferred by programmers in the art to which the present disclosure belongs.

While the present disclosure has been described and illustrated by way of preferred embodiments, those skilled in the art will recognize that various modifications and changes can be made without departing from the spirit and scope of the appended claims.

Claims

A method for adjusting the acceptance judgment condition for an inspection object in a delivery determination device including a database, a processing unit, a user input unit and an output unit,

Acquiring, by the processing unit, measured values of structures of a plurality of test objects from the database;

Judging, by the processing unit, good or bad for each test object of the plurality of test objects by comparing an error value of the measured value with respect to a design value of the structure and a predetermined reference value;

Identifying, by the processing unit, one or more inspected objects in which a determination error has occurred among the plurality of inspected objects, wherein the determination error includes a first error in which the inspection object determined to be good is identified as defective and an inspection object determined to be defective. Includes a second error identified as good;

The processing unit generates a test result graph including the number of the plurality of test objects according to the error value, the reference value, and the number of one or more test objects in which the determination error occurred, and through the output unit, the test result graph Outputting;

Updating, by the processing unit, the reference value according to a graphical input through the user input unit on the inspection result graph such that the number of one or more inspection objects in which the determination error occurred is reduced; And

And judging, by the processing unit, the good or bad for each specimen of the plurality of specimens by comparing the error value with the updated reference value.
The method of claim 1,

The judging step,

Identifying a specimen in which a trial error has occurred among the plurality of specimens; And

Displaying the updated reference value and the number of specimens in which the judicial error has occurred in the examination result graph.
The method of claim 1,

Identifying the one or more inspected objects includes receiving a decision review result indicating whether the plurality of inspected objects has the first error or the second error.
The method of claim 1,

Identifying the one or more specimens,

Determining a good error value distribution based on the distribution of the error values;

Estimating a good error value range from the good error value distribution; And

Identifying the first error or the second error based on the good error value range.
The method of claim 1,

The updating of the reference value may include receiving the graphic input for moving the position of the GUI object representing the reference value on the test result graph, or the graphic input for designating the position of the updated reference value on the test result graph. Comprising the steps of:
The method of claim 1,

The outputting the test result graph may include:

Outputting a two-dimensional graph as the inspection result graph, the horizontal axis representing the error value, and the vertical axis representing the number of test objects having a corresponding error value among the plurality of test objects; And

Displaying, on the inspection result graph, a bar-shaped GUI object that is movable by a user manipulation as the reference value.
The method of claim 1,

The outputting the test result graph may include:

Determining at least one candidate reference value such that the number of one or more specimens in which the determination error has occurred is minimized; And

Displaying the candidate reference value on the test result graph.
The method of claim 7, wherein

The determining of the candidate reference value includes determining, as the candidate reference value, at least one of values greater than or equal to the maximum value of the good error value range based on the good error value range stored in the database.

And the good error value range is estimated based on the distribution of the error values by a judgment review device.
The method of claim 7, wherein

The determining of the candidate reference value may include greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range based on the good error value range and the bad error value range stored in the database. Determining at least one of the values as the candidate reference value,

And the good error value range and the bad error value range are estimated by the determination review apparatus based on the distribution of the error values.
A non-transitory computer readable recording medium having recorded thereon a program for execution on a computer, wherein the program is executed by the processor when executed by the processor.

Obtaining measurement values of structures of the plurality of test objects from a database;

Comparing the error value of the measured value with respect to the design value of the structure and a predetermined reference value to determine good or bad for each test object of the plurality of test objects;

Identifying one or more inspected objects in which a determination error has occurred among the plurality of inspected objects, wherein the determination error is a first error in which the inspection object determined to be good is identified as defective and a specimen in which the inspection object determined to be defective is identified as good; Contains 2 errors-;

Generating a test result graph including the number of the plurality of test objects according to the error value, the reference value, and the number of one or more test objects in which the determination error occurs, and outputting the test result graph through the output unit;

Updating the reference value according to a graphical input through the user input unit on the inspection result graph such that the number of one or more inspected objects in which the determination error occurs is reduced; And

Judging good or bad for each specimen of the plurality of specimens by comparing the error value and the updated reference value

And a computer readable medium comprising executable instructions for causing the computer to perform the operation.
In the apparatus for adjusting the conditions of acceptance judgment for the test object,

A database storing design values and measured values of structures of a plurality of test objects;

A determination unit for comparing the error value of the measured value with respect to the design value with a predetermined reference value to determine good or bad for each test object of the plurality of test objects;

A test result identifying one or more test objects in which a determination error has occurred among the plurality of test objects, and including a number of the plurality of test objects according to the error value, the reference value, and the number of one or more test objects in which the determination error occurred; A determination result generating unit for outputting a graph, wherein the determination error includes a first error in which the test object determined to be good is identified as defective and a second error in which the test object determined to be defective is identified as good; And

A determination criterion adjusting unit for updating the reference value according to a graphical input on the inspection result graph such that the number of one or more inspection objects in which the determination error has occurred is reduced;

And the acceptance judgment unit compares the error value with the updated reference value to judge good or bad of the plurality of test objects.
The method of claim 11,

The acceptance determination unit identifies a test object in which a trial error has occurred among the plurality of test objects,

And the determination result generation unit displays the updated reference value and the number of inspection objects in which the judging error has occurred in the inspection result graph.
The method of claim 11,

And a communication unit which receives a determination review result indicating whether the plurality of test objects have the first error or the second error.
The method of claim 11,

The determination result generation unit,

Determine a good error value distribution based on the distribution of the error values,

Estimate a good error value range from the good error value distribution,

And identify the first error or the second error based on the good error value range.
The method of claim 11,

The determination criterion adjusting unit is configured to respond to the graphical input for moving the position of the GUI object representing the reference value on the inspection result graph or the graphical input for specifying the position of the updated reference value on the inspection result graph. Device for updating the reference value.
The method of claim 11,

The determination result generation unit outputs a two-dimensional graph as the inspection result graph, in which the horizontal axis represents the error value, and the vertical axis represents the number of inspection objects having the corresponding error value among the plurality of inspection objects.

And a bar-shaped GUI object movable on the graph of the inspection result as the reference value.
The method of claim 11,

And the determination result generation unit displays at least one candidate reference value on the inspection result graph such that the number of one or more inspection objects in which the determination error occurred is minimized.
The method of claim 17,

The database further stores a good error value range,

The determination result generator determines, based on the good error value range stored in the database, at least one of values greater than or equal to the maximum value of the good error value range as the candidate reference value.

And said good error value range is estimated by the determination review apparatus based on the distribution of said error values.
The method of claim 17,

The database further stores a good error range and a bad error range,

The determination result generation unit may be greater than or equal to the maximum value of the good error value range and less than or equal to the minimum value of the bad error value range based on the good error value range and the bad error value range stored in the database. Determining at least one of the candidate reference values,

And the good error value range and the bad error value range are estimated by the determination review apparatus based on the distribution of the error values.
The method of claim 11,

And the determination result generating unit enlarges and outputs the predetermined region in response to an input for setting a predetermined region on the inspection result graph.